Information processing device, information processing method

Abstract

To provide an information processing device capable of properly comprehending effect of an exchange event through analysis of changes in an exchange state between participants during a period of the exchange event.SOLUTION: An information processing device estimates an exchange state between visitors on the basis of positions of the visitors visiting a specific area during a period of an event, and based on the estimated result analyses whether or not the level of exchange between the visitors changed during the period of the event.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a technique for analyzing the state of interaction between visitors at a social event. [Background technology]

[0002] Exchange events aimed at forming exchange networks among participants are, for example, organized by local government agencies. Organizers generally estimate the effectiveness of an exchange event (e.g., whether or not there is active exchange among participants) by experiencing the atmosphere at the exchange venue.

[0003] On the other hand, it is possible to infer whether participants are interacting by tracking their locations. The following Patent Document 1, although not related to social events, describes a technology for estimating an interaction area based on location information of mobile objects in a local community. It is also possible to use a similar technology in social events.

[0004] Specifically, the following Patent Document 1 aims to "provide a stay tendency analysis device or the like that generates a stay tendency network between an unspecified number of mobile objects belonging to different communities," and describes a technology that "includes a regional attribute setting means for setting a regional attribute for each regional range, a mobile object stay degree calculation means for calculating, for each mobile object, a mobile object stay degree that moved to and stayed in each of other regional ranges other than the regional range of the mobile object's belonging position for a predetermined period, a regional stay degree calculation means for calculating, for each regional range, a regional stay degree that multiple mobile objects that belong to the regional range moved to and stayed in each of the other regional ranges, a stay tendency network generation means for generating a stay tendency network in which the regional ranges are nodes and the regional stay degrees between the regional ranges and the other regional ranges are connected as links, and an exchange regional range determination means for determining, as an exchange regional range, regional ranges that connect regional ranges of different regional attributes with each other by links" (see abstract). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2023-063979 A Summary of the Invention [Problem to be solved by the invention]

[0006] Whether or not a participant is interacting with other participants in a social event can be inferred based on the participant's location. For example, participants who are in close proximity to each other for a certain period of time are inferred to be interacting with each other. Conventional techniques such as Patent Document 1 are also generally based on this idea. However, although this method can infer whether or not participants are interacting at a certain moment, there is a possibility that the interaction is temporary and limited to the event.

[0007] For example, if a social event generates new interactions between participants who did not previously have interactions, the organizer of the social event can determine that the event was effective. Alternatively, if interactions that occurred during the social event continue even after the event ends, the organizer can determine that the event was effective. However, simply identifying that interactions occurred instantaneously during the event period makes it difficult to determine changes in the state of interactions before and after the event period.

[0008] The present invention has been made in consideration of the above-mentioned problems, and aims to properly grasp the effectiveness of a social event by analyzing changes in the interaction state between participants during the social event. [Means for solving the problem]

[0009] The information processing device of the present invention estimates the state of interaction between visitors who visited a specific area during a specified period based on the locations of the visitors, and analyzes whether the degree of interaction between the visitors has changed during the specified period based on the estimation results. Effect of the Invention

[0010] According to the information processing device of the present invention, the effect of the social event can be appropriately understood by analyzing the change in the state of interaction between the participants during the social event. Problems, configurations, advantages, etc. other than those described above will become clear from the description of the following embodiment. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a plan view showing a schematic diagram of participants interacting with each other at a social event. [Diagram 2] 1 is a block diagram of an information processing device 1 according to a first embodiment of the present invention. [Diagram 3] This is an example showing the results of an analysis of the interaction occurrence rate by participant attributes. [Figure 4] 13 shows an example of the results of analysis performed by the analysis unit 14 on correlations between attributes of participants who interacted with each other during the event period. [Diagram 5] The results of calculating statistics on the attributes of participants who had interactions are shown below. [Figure 6] An example of the results of measuring the continuity of interaction by attribute type within the participant cluster where the interaction occurred is shown below. [Figure 7] An example of the results of an analysis of the correlation between sales of merchandise related to a networking event and whether or not networking event attendees who purchased the merchandise interacted with other attendees at the networking event is shown below. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] <Embodiment 1> Figure 1 is a plan view that shows a model of participants interacting at a social event. The horizontal axis of the figure represents the passage of time. The circles in the figure represent each participant. Before the event, participants (visitors to the event venue) are sparsely gathered, but during the event, some participants interact with each other, and even after the event period ends, they remain at the event venue and continue to interact.

[0013] When two or more participants come within a certain distance and this state continues for a certain period of time, it can be assumed that interaction has occurred between the participants. By using this concept in conventional technology, it is believed that it is possible to identify whether participants are interacting with each other during the event period. However, this only determines whether participants are interacting with each other at the moment the identification process is performed, and does not determine how the interaction state has changed before and after the event period.

[0014] In other words, the prior art has not adequately considered determining changes in interaction status, such as: (a) new interactions occur during the event period between participants who did not interact before the event period; (b) participants who interacted during the event period continue to interact with each other after the event period.

[0015] Therefore, the present invention provides a method for estimating how interactions between participants change before and after the event period, which is believed to make it possible to more appropriately grasp the effects of a social event.

[0016] It should be noted that the criteria for determining whether or not an interaction has occurred may vary depending on the manner of communication between participants and the nature of the event. For example, assume a situation in which a group of participants who have had a conversation at an event venue stay at the event venue and continue to talk even after the event ends. In this case, if the state in which the distance between the participants approaches within a threshold distance continues for a threshold time or more from an arbitrary starting point (e.g., the end of the event), it can be deemed that an interaction has occurred between the participants. Alternatively, assuming a matching event, it can be determined whether or not an interaction has occurred depending on whether matching is successful. For example, if participants who have not had an interaction before approach each other within a threshold distance during the event period, and the participants approach each other again within an appropriate judgment criterion period after the event ends (e.g., three months after the event ends) a threshold number of times or more (e.g., three times), it can be deemed that an interaction has occurred between the participants. The estimation unit 13 and the analysis unit 14 described later can estimate the interaction state by these methods.

[0017] Fig. 2 is a block diagram of an information processing device 1 according to a first embodiment of the present invention. The information processing device 1 is a device that analyzes the state of interaction between participants in an interaction event as exemplified in Fig. 1 and its change over time. The information processing device 1 includes an identification unit 11, an acquisition unit 12, an estimation unit 13, an analysis unit 14, and an output unit 15.

[0018] The identification unit 11 identifies participants who have come to the event venue during the event period. For example, each participant is identified by acquiring identification information of the participant. As the identification information of the participant, for example, a unique ID may be assigned to a participant who has arrived at the event venue, and the ID may be used as identification information to identify each participant. For example, identification information can be assigned to a participant by storing the identification information in advance in an RFID tag or the like and distributing the tag to the participant. Alternatively, since the mobile terminal carried by the participant has a unique ID, the participant can be individually identified by identifying the unique ID via the communication line carrier. Any other method may be used.

[0019] When acquiring the ID of a mobile device via a communication line operator, for example, based on the location information of the mobile device, it is possible to identify users who are located at the event location during the event as participants. This makes it possible to provide analysis results by identifying participants from data based on the location information of the mobile device, and to provide analysis data without the organizer and data provider exchanging personal information.

[0020] The specification unit 11 may acquire attribute information (such as name, date of birth, and gender) of the participants in addition to the identification information. Specific examples of the attribute information and how to use it will be described later. Means for acquiring the attribute information will also be described later.

[0021] The acquisition unit 12 acquires the positions of the participants identified by the identification unit 11. For example, if RFID tags are distributed to the participants, the position of each participant can be acquired by measuring the position of the tag. If a unique ID of a mobile terminal is used, the position of the participant can be acquired via a position acquisition system (e.g., Global Positioning System) provided in the mobile terminal. Any other method may be used.

[0022] The estimation unit 13 estimates the interaction state of the participants based on the positions acquired by the acquisition unit 12. The interaction state here indicates whether or not the participants are instantaneously communicating with each other. As explained above, even if instantaneous communication is performed, it cannot be said that an interaction has occurred immediately, and some judgment criterion is required to consider that an interaction has occurred. The estimation unit 13 estimates whether or not instantaneous communication has occurred as a prerequisite for the occurrence of an interaction. Whether or not communication has occurred can be determined, for example, by the distance between the participants approaching within a threshold distance.

[0023] The analysis unit 14 analyzes how the state of interaction between the participants has changed over time during the event period based on the estimation result by the estimation unit 13. For example, whether or not there is interaction between the participants before the event period is grasped in advance based on the estimation result by the estimation unit 13. When the estimation unit 13 estimates that new communication has occurred between participants who have no interaction, it is determined whether or not it can be said that interaction has occurred between the participants. As described in FIG. 1, the criteria for determining whether or not interaction has occurred may vary depending on the mode of communication between the participants and the nature of the event, so appropriate criteria are used depending on these. The same criteria can also be used for determining whether or not there is interaction before the event period. In the case of interaction between three or more participants, for example, the maximum value (or other appropriate value such as the average value) of the distance between the participants in all combinations of the participants may be used.

[0024] The analysis unit 14 may determine that the interaction continues even if some of the participants who have had an interaction leave the interaction. For example, the interaction group on the far right in FIG. 1 was a group of four people during the event period, but became a group of three people after the event. Even in this case, it may be considered that at least three of the four people continue to have an interaction.

[0025] The output unit 15 outputs at least one of the estimation result by the estimation unit 13 or the analysis result by the analysis unit 14 (it may output both). As an output means, for example, data describing the estimation result or the analysis result may be output, or a screen display visually expressing the same data may be generated and displayed on an arbitrary display device. Any other appropriate output method may be used.

[0026] In addition to the above, the analysis unit 14 may determine whether or not the degree of interaction between participants who have been interacting with each other for a long time has changed during the event period. For example, the frequency of communication (the number of times the distance between participants is within a threshold distance) before the event is grasped in advance, and the frequency of communication may be analyzed to determine whether communication has increased or decreased after the event period. This allows appropriate analysis of not only newly occurring interactions, but also how the degree of existing interactions has changed.

[0027] <First embodiment: Summary> The information processing device 1 according to the first embodiment estimates the interaction state between the participants based on the locations of the event participants who came to the event venue during the event period, and analyzes the change over time in the interaction state. This makes it possible to grasp whether the interaction between the participants that occurred during the event period continues after the event period. Therefore, it is possible to more appropriately grasp the effect of the interaction event.

[0028] <Embodiment 2> In the first embodiment, an example is described in which whether or not an interaction has occurred is analyzed based on whether or not communication continues after the event period. The information processing device 1 may also analyze the cause of the occurrence of an interaction in addition to the continuation of communication after the event. Therefore, in the second embodiment of the present invention, an example is described in which an interaction occurrence rate is analyzed for each attribute of the participants. The configuration of the information processing device 1 is the same as that of the first embodiment.

[0029] FIG. 3 is an example showing the result of analyzing the interaction occurrence rate for each attribute of the participant. In the above embodiment, the analysis unit 14 may classify the participants according to the attributes of the participants, and then calculate the interaction occurrence rate for each classification. For example, the participants may be classified into age groups according to their age, and the interaction occurrence rate may be calculated for each age group. In the example shown in FIG. 3, it can be seen that the interaction occurrence rates (the ratio of the number of participants in which an interaction occurred within the age group to the number of participants belonging to the same age group) of the 20s group and the 30s group tend to be higher than other age groups. Other attributes such as gender and annual income can be calculated in the same manner. The output unit 15 can output the calculation result by the analysis unit 14 in a format such as that shown in FIG. 3.

[0030] Other attributes that can be used to understand the trend of the interaction occurrence rate include occupation, residential area, etc. As in Figure 3, the attribute value can be plotted on the horizontal axis and the interaction occurrence rate on the vertical axis.

[0031] Instead of the interaction rate, the number of interactions may be calculated. However, because the absolute number of participants differs for each attribute group (age group, gender group, etc.), the number of interactions will also be affected by that absolute number. For example, keep in mind that there may be cases where the interaction rate is high for the 30s group, but the number of participants is smaller than that of the 20s group, so the number of interactions is higher for the 20s group than for the 30s group.

[0032] The attributes of the participants can be acquired, for example, in the following manner. For example, the participants self-declare their age at the reception of the social event, or a similar input form is provided on the participation reception website. Gender and annual income can also be acquired in a similar manner. The acquired attributes may be recorded in an RFID tag distributed to the participants, or may be recorded in the information processing device 1 in association with the identification ID of the participants. When using a unique ID of the mobile terminal carried by the participant, the attributes of the participant can be acquired from the information of the line subscriber corresponding to that ID. That is, the attributes can be acquired from a database (a storage device that stores data) that describes the attribute information of the line subscribers.

[0033] <Embodiment 3> In the second embodiment, an example is described in which the interaction occurrence rate is analyzed for each participant attribute in order to analyze the cause of the interaction. This makes it possible to grasp the tendency of participant attributes that are likely to cause interaction. On the other hand, whether or not an interaction occurs may also be influenced by the correlation of attributes between participants. For example, even if participants in their twenties tend to have interactions, the attributes of participants with whom they can interact may be somewhat limited (e.g., participants of the same age are more likely to have interactions). Therefore, in the third embodiment of the present invention, an example is described in which the correlation of attributes between participants with whom an interaction has occurred is analyzed. The configuration of the information processing device 1 is the same as that of the first and second embodiments.

[0034] FIG. 4 shows an example of the results of an analysis performed by the analysis unit 14 on the correlation of attributes between participants who interacted during the event period. Here, age and assets are considered as attributes of participants, and these are used as the vertical and horizontal axes. The circles in FIG. 4 are plots of the attributes of each participant. The plots can be output by the output unit 15, for example.

[0035] According to the example shown in Figure 4, it can be inferred that there is a tendency for interactions to occur easily between participants of similar ages, even if their assets differ greatly. This is because the participants in interaction groups 1 and 2 are both around 25 years old, and their assets differ greatly. On the other hand, in interaction group 3, although the participants' ages differ greatly, all of their assets are around 10 million. Therefore, it can be inferred that there is a tendency for interactions to occur easily between participants of similar assets, even if their ages differ greatly. Furthermore, such trends can be used when considering seating arrangements and discussion group attributes for the next interaction event.

[0036] Similar analyses can be performed for other attributes. Similar analyses may be performed for one or more than two attributes. Other examples of attributes to be analyzed include annual income, residential area, shopping history, and the like. The shopping history here refers to the degree of purchase of a product (an index showing the strength of the willingness to purchase the product, such as purchase frequency, purchase amount, or number of items purchased, etc.) when a product related to a social event is sold. By analyzing the correlation with the degree of purchase of event-related products, it is considered possible to infer, for example, the relationship between the strength of attachment to the event and the occurrence of social interactions. Alternatively, it is considered possible to analyze a tendency that participants who purchase certain products often have interactions with each other.

[0037] The correlation between the participant attributes as shown in Fig. 4 may be determined by the user by the output unit 15 outputting a plot as shown in Fig. 4, or may be quantitatively evaluated by the analysis unit 14. For example, the analysis unit 14 may calculate the age difference between the participants belonging to the same interaction group and analyze the relationship between the age difference and the occurrence of interaction. A specific example will be described again in the embodiment described later.

[0038] <Fourth embodiment> In the first embodiment, an example has been described in which the analysis unit 14 analyzes whether or not an interaction has occurred between the participants. In addition to the above, the analysis unit 14 may determine the degree of interaction continuity between the participants. The degree of interaction continuity here is an evaluation index that indicates the degree to which an interaction between the participants that occurred during the event period continues even after the event period. For example, the duration of an interaction that occurred during the event period can be measured starting from the end of the event, and the duration can be used as the degree of interaction continuity. However, it should be noted that the degree of interaction continuity is calculated only between participants who are determined to have an interaction.

[0039] It is considered possible that the degree of interaction continuity may have a certain tendency depending on the attributes of the participants. For example, it is assumed that the smaller the age difference between the participants who are interacting, the easier it is for the interaction to continue. Therefore, in the fourth embodiment of the present invention, an example of grasping such a tendency by further analyzing the attributes of the participants with whom the interaction occurred will be described. The configuration of the information processing device 1 is the same as that of the first to third embodiments.

[0040] Fig. 5 shows the results of calculating statistics on the attributes of participants who have interacted with each other. Here, an example is shown in which age, gender, and economic status (e.g., annual income) are acquired as the attributes of the participants. The analysis unit 14 regards a group of participants who have interacted with each other as one cluster, and calculates statistics on the attributes of the participants for each cluster.

[0041] Regarding age, for example, the age difference between the maximum and minimum ages of the participants in a cluster can be regarded as a statistic regarding age in that cluster. Regarding gender, for example, the male / female ratio of participants in a cluster can be regarded as a statistic regarding gender in that cluster. Regarding annual income, for example, the difference in annual income between the maximum and minimum annual incomes of participants in a cluster can be regarded as a statistic regarding annual income in that cluster. Figure 5 also shows the results of calculating these statistics.

[0042] As another attribute, the degree of purchase of a product related to a particular event can be used. For example, it is expected that at a pet-related event, people who own multiple pets and purchase products frequently tend to continue to interact with each other, and at an investment event, people who purchase less financial products tend to seek information and continue to interact with each other. By using the degree of product purchase as an attribute, it is possible to analyze the degree of interaction continuity.

[0043] Cluster 1 is a group of participants who interacted during the event, and the interaction continued after the event. Cluster 2 also continues to interact, but one participant left the interaction during or after the event. Therefore, for convenience, sub-numbers were added to the cluster numbers to distinguish between clusters during and after the event, and statistics were calculated for each sub-number. Cluster 3 is a group of participants who interacted during the event, but did not continue to interact after the event.

[0044] FIG. 6 shows an example of the results of measuring the degree of interaction continuity for each attribute type within a participant cluster where an interaction occurred. As explained in the first embodiment, for example, the degree of interaction continuity is the length of time that an interaction continues after the event period, starting from the end of the event. The analysis unit 14 can create a histogram of the degree of interaction continuity for each type of attribute statistics (age difference, male / female ratio, annual income difference) explained in FIG. 5. The output unit 15 outputs the results. FIG. 4 shows an example of output displayed on a screen.

[0045] Regarding age difference, for example, the average degree of interaction continuity is calculated for clusters with an age difference of 0 to 10, and the result is used as the vertical axis for the group with an age difference of "0 to 10" in Figure 4. Similar calculations are performed for other age difference groups. In the example shown in Figure 6, it can be seen that the smaller the age difference in a cluster, the greater the degree of interaction continuity.

[0046] For the gender ratio, for example, the horizontal axis is the number of men / females in a cluster. For example, the average degree of interaction continuity is calculated for clusters with a gender ratio of 0.5-1.0, and the result is used as the vertical axis for the group with a gender ratio of "0.5-1.0" in Figure 6. Similar calculations are performed for other gender ratio groups. In the example shown in Figure 6, it can be seen that the degree of interaction continuity tends to be greater in clusters with roughly equal numbers of men and women.

[0047] <Embodiment 4: Summary> The information processing device 1 according to the fourth embodiment calculates statistics of participant attributes in a participant cluster where an interaction occurred, and outputs the interaction continuity for each type of statistics. This makes it possible to grasp the tendency of an interaction to continue even after the event ends for each participant attribute. Therefore, the effect of the interaction event can be analyzed in more detail.

[0048] <Embodiment 5> In the above embodiments, the effects of a social event have been described as the occurrence of social interaction or the continuation of social interaction after the event. In a social event, in addition to the interaction between participants, products related to the event may be sold. Sales of such products can also be considered as an effect of the social event. Therefore, in the fifth embodiment of the present invention, an example of analyzing product sales related to a social event will be described. The configuration of the information processing device 1 is the same as in the first to fourth embodiments.

[0049] Figure 7 shows an example of the results of an analysis of the correlation between sales of products related to a social event and whether or not a social event participant who purchased the product interacted with other participants at the social event. Products related to social events are typically products sold at the event venue, but are not necessarily limited to this and may be sold on the event's website or the like. In other words, as long as the product is sold in connection with the social event (the purchaser purchases the product after recognizing that it is related to the social event), the sales method can be arbitrary.

[0050] The analysis unit 14 tally up the sales of products purchased by event participants, including the sales of products purchased by those who had interactions during the event period and the sales of products purchased by those who did not. This makes it possible to analyze the correlation between event-related product sales, those who had interactions, and those who did not have interactions. For example, the tallying results shown in FIG. 7 show that those who had interactions purchased more event-related products. Therefore, it can be predicted that sales of event-related products will increase by promoting interactions between participants during the event.

[0051] When a participant purchases a product, the participant ID and the product purchase amount (or information that can identify the purchase amount, such as the product ID and quantity) are associated and recorded in the information processing device 1, making it possible to identify whether the participant who purchased the product is an interaction generator. This is because the participant ID of a participant who has generated an interaction is also identified when identifying his or her location. The analysis unit 14 can calculate statistical values ​​such as those shown in FIG. 7 according to the association between the ID of the participant who purchased the product and the product purchase amount. The output unit 15 outputs the results.

[0052] <Modifications of the present invention> The present invention is not limited to the above-described embodiment, and includes various modified examples. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to those having all of the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0053] In the above embodiment, an example has been described in which an RFID tag or a unique ID of a mobile terminal is used as identification information of a participant, but any other identification method may be used. For example, a participant may be individually identified by face recognition.

[0054] In the second embodiment, when calculating the interaction occurrence rate (or the number of interaction occurrences), it is possible to count whether or not there is interaction for all combinations between participants, or to count one cluster as one interaction occurrence as described in the fourth embodiment. It is also possible to count the presence or absence of interaction for each other appropriate unit.

[0055] In the third embodiment, it has been described that the correlation between attributes is analyzed by plotting the participants in the group in which the interaction occurred for each attribute. Since the numerical values ​​of the attributes may vary greatly depending on the nature of the attributes, the numerical values ​​of the attributes may be normalized before performing a similar analysis. For example, the normalized values ​​of each attribute may be used to calculate a correlation coefficient (e.g., age and trial calculation) for each group, and an analysis for each group may be performed.

[0056] In the fourth embodiment, the age difference is exemplified as a statistic of the age of participants in a cluster, but other statistics related to the age of participants may be used. For example, the average age or variance of participants in a cluster may be considered. When the average age is used, the relationship between the average age of the cluster and the degree of exchange continuity can be analyzed. When the age variance is used, the relationship between the variation in age in a cluster and the degree of exchange continuity can be analyzed. Similarly, other statistics related to gender and economic power may be used for the male-female ratio and annual income difference. For example, the average annual income or total annual income of participants in a cluster represents the economic level of the cluster as a whole. This statistic can be used to analyze whether the degree of exchange continuity is affected by the high economic level.

[0057] In the above embodiments, each functional unit (identification unit 11, acquisition unit 12, estimation unit 13, analysis unit 14, output unit 15) of the information processing device 1 can be configured by hardware such as a circuit device that implements these functions, or can be configured by a calculation device such as a CPU (Central Processing Unit) executing software that implements these functions. [Explanation of symbols]

[0058] 1: Information processing device 11: Specific part 12: Acquisition part 13: Estimation part 14:Analysis Department 15: Output section

Claims

1. An identification unit that identifies visitors who visited a specific area during a specified period; an acquisition unit for acquiring the location of the identified visitor; an estimation unit that estimates an interaction state of the plurality of attendees based on the positions of the plurality of attendees; an analysis unit that analyzes whether or not a degree of interaction between the plurality of visitors has changed during the predetermined period based on a result of the estimation by the estimation unit; an output unit that outputs an analysis result by the analysis unit; Equipped 23. An information processing apparatus comprising:

2. The analysis unit analyzes whether or not a degree of interaction between the plurality of visitors has changed during the predetermined period based on the interaction state of the plurality of visitors before the predetermined period and the interaction state of the plurality of visitors after the predetermined period.

2. The information processing apparatus according to claim 1,

3. The analysis unit analyzes whether or not a new interaction has occurred during the specified period between the visitors who did not have an interaction before the specified period, based on the interaction state of the plurality of visitors before the specified period and the interaction state of the plurality of visitors after the specified period.

2. The information processing apparatus according to claim 1,

4. the estimation unit, when a state in which the positions of the plurality of attendees are within a predetermined distance from each other continues for a predetermined time or more, estimates that an interaction is taking place between the plurality of attendees as the interaction state; or The estimation unit estimates that the plurality of visitors who have interacted during the predetermined period are interacting with each other if a state in which the distance between the plurality of visitors again falls within a threshold distance occurs a threshold number of times or more within a reference period after the predetermined period.

2. The information processing apparatus according to claim 1,

5. The output unit outputs at least one of the number of interactions between the visitors and the ratio of the visitors to the number of interactions between the visitors.

2. The information processing apparatus according to claim 1,

6. The identification unit identifies the attributes by acquiring the attributes from a storage unit that stores data describing the attributes of the visitor; The output unit categorizes the visitors according to the attributes and outputs the number or the ratio for each categorization.

6. The information processing apparatus according to claim 5,

7. The identification unit identifies the attributes by acquiring the attributes from a storage unit that stores data describing the attributes of the visitor; The analysis unit analyzes the degree of correlation of the attributes between the visitors who have had an exchange.

2. The information processing apparatus according to claim 1,

8. the predetermined period is a period during which a specific event is held, The identification unit may include, as the attribute, The age of the attendees, the assets of the attendees, the residential area of ​​the attendees, the frequency with which the attendees purchase merchandise related to the event, Identify at least one of the following: The analysis unit analyzes the degree of correlation for each type of the attribute identified by the identification unit.

8. The information processing apparatus according to claim 7,

9. The analysis unit plots differences in the attributes between the visitors who have had interactions for each type of attribute, The analysis unit analyzes the relative relationship between the plotted attributes to analyze the degree of correlation of the attributes between the visitors with whom an interaction has occurred.

8. The information processing apparatus according to claim 7,

10. The analysis unit analyzes whether the plurality of visitors who have had an interaction during the specified period continue to have an interaction after the specified period based on whether the interaction state between the plurality of visitors estimated by the estimation unit to be having an interaction continues after the specified period.

2. The information processing apparatus according to claim 1,

11. The analysis unit determines an interaction continuity indicating a degree to which the plurality of attendees continue to interact with each other even after the predetermined period.

2. The information processing apparatus according to claim 1,

12. The analysis unit determines the degree of interaction continuity based on a length of time that the plurality of visitors continue to interact after the predetermined period.

12. The information processing apparatus according to claim 11,

13. The identification unit identifies the attributes by acquiring the attributes from a storage unit that stores data describing the attributes of the visitor; The analysis unit calculates statistics regarding the attributes of each of the plurality of visitors who continue to have interactions with each other, The output unit outputs a relationship between the statistical amount and the interaction continuity for each type of the statistical amount.

12. The information processing apparatus according to claim 11,

14. The identification unit may include, as the attribute, The age of the visitor, the gender of the visitor, the economic power of the visitor, the degree to which the visitor purchases products related to a specific event held in the specific area, Identify at least one of the following: The analysis unit may, as the statistics, Statistics regarding the ages of the plurality of visitors with whom the interaction continues; Statistics regarding the gender of the plurality of visitors with whom the interaction continues; Statistics regarding the economic strength of the plurality of visitors with whom they continue to interact; Statistics regarding the purchase frequency of the products of the plurality of visitors who continue to interact with each other; Identify at least one of the following:

14. The information processing apparatus according to claim 13,

15. the predetermined period is a period during which a specific event is held, The analysis unit analyzes a correspondence relationship between sales of products related to the event, attendees who had interactions at the event, and attendees who did not have interactions at the event.

2. The information processing apparatus according to claim 1,

16. Identifying visitors who visited a particular area during a given period of time; obtaining a location of the identified attendee; estimating an interaction state of the plurality of attendees based on the locations of the plurality of attendees; analyzing whether a degree of interaction between the plurality of visitors has changed during the predetermined period based on the estimation result in the estimating step; outputting an analysis result from the analyzing step; have 23. An information processing method comprising:

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